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International Journal of Technology and Modeling
Published by Etunas Sukses Sistem
ISSN : -     EISSN : 29646847     DOI : https://doi.org/10.63876/ijtm
Core Subject : Science, Education, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics
International Journal of Technology and Modeling (e-ISSN: 2964-6847) is a peer-reviewed journal as a publication media for research results that support research and development of technology and modeling published by Etunas Sukses Sistem. International Journal of Technology and Modeling is published every four months (April, August, December). This journal is expected to be a vehicle for publishing research results from practitioners, academics, authorities, and related communities. IJTM aims to publish high-quality, original research, theoretical studies, and practical applications while promoting a global perspective on technology and modeling. The journal is dedicated to providing a forum for knowledge exchange and fostering cross-disciplinary collaboration, ensuring that research published within its pages contributes to the advancement of science and technology worldwide.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 4 No. 2 (2025)" : 5 Documents clear
Implementing LU Decomposition to Improve Computer Network Performance Angelia; Bandiyah, Salza Nur; Marine, Yoni
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.101

Abstract

The application of LU decomposition in computer networks has great potential to improve system performance, especially in processing and analyzing complex and large-sized data. LU decomposition is a technique in linear algebra that breaks down a matrix into two triangular matrices, namely the lower (L) and upper (U) matrices, which facilitates the solution of a system of linear equations. In the context of computer networks, these algorithms can be applied to accelerate the analysis and processing of network traffic data, resource management, and traffic scheduling. Large matrices are often used to model networks in applications such as route mapping, bandwidth allocation, and network performance monitoring. The use of LU decomposition allows efficiency in handling such big data, speeds up calculations and reduces latency time in network information processing. This study proposes the application of LU decomposition to optimize several aspects in computer networks, such as dynamic routing, network fault detection, and more effective resource allocation. With LU decomposition, the process of load analysis and problem identification can be carried out more quickly, increasing the throughput and stability of the system. The results of the experiments conducted show that the application of LU decomposition can reduce the computational load and accelerate the system's response to changes in network conditions. Overall, the application of these methods can contribute to improving the efficiency and performance of modern computer networks, especially in the face of increasingly high and complex data traffic demands.
Revolutionizing Industries: The Role of Technological Innovations in Modern Business Practices Nkrumah, Kwame; Agyemang, Akosua
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.136

Abstract

Technological innovation has emerged as a critical driver in transforming modern business practices across the globe. This study investigates the extent to which technological advancements have reshaped industrial operations and business strategies within the Cameroonian context. Using a mixed-methods approach, we collected data from 150 businesses across various sectors, complemented by in-depth interviews with industry leaders and technology stakeholders. The findings reveal a strong correlation between the adoption of emerging technologies—such as artificial intelligence, cloud computing, and automation—and improvements in productivity, operational efficiency, and market competitiveness. However, the study also highlights persistent challenges, including infrastructure deficits, limited digital literacy, and regulatory constraints that hinder full-scale adoption. Our analysis underscores the need for targeted policy reforms, capacity-building initiatives, and strategic investments to foster a more innovation-friendly ecosystem. This research contributes to the growing body of knowledge on digital transformation in emerging economies and offers actionable insights for business leaders, policymakers, and development practitioners aiming to harness technology for sustainable industrial growth.
A Survey on Deep Learning for Natural Language Processing: Models, Techniques, and Open Research Problems Hào, Nguyễn Nhật; Vy, Trần Khánh; Phước, Lê Văn
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.137

Abstract

In recent years, deep learning has emerged as a powerful paradigm in natural language processing (NLP), enabling significant breakthroughs in tasks such as machine translation, sentiment analysis, and question answering. This survey provides a comprehensive overview of deep learning models and techniques that have shaped the evolution of NLP, with a focused lens on the Vietnamese language as a representative low-resource language. We review foundational models including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and Transformer-based architectures such as BERT and GPT, and analyze their applications in Vietnamese NLP tasks. Special attention is given to the development and adaptation of Vietnamese-specific pretrained language models like PhoBERT and ViT5, as well as the use of multilingual approaches to address data scarcity. In addition, the paper discusses practical implementations in Vietnam, such as sentiment analysis of social media, Vietnamese question answering systems, and machine translation, highlighting the opportunities and challenges in this context. We also identify open research problems including limited training data, dialectal variations, code-switching, and ethical concerns, offering insights and directions for future work. This survey aims to serve as a resource for researchers and practitioners seeking to advance NLP capabilities in low-resource languages using deep learning.
Optimizing Supply Chains Through Technology and Computational Modeling Capulong, Alyssa Jean; Shah, Nisha
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.138

Abstract

In the era of rapid globalization, optimizing supply chains has become essential for enhancing operational efficiency and competitiveness. This study investigates the role of technology and computational modeling in improving supply chain performance in the context of Myanmar, a developing economy with unique logistical and infrastructural challenges. By integrating advanced technologies such as IoT, data analytics, and simulation-based modeling, the research evaluates their impact on demand forecasting, inventory management, and transportation planning. A case study approach involving key sectors such as agriculture and manufacturing was employed to assess real-world applicability. Results indicate significant improvements in supply chain responsiveness, cost reduction, and decision-making accuracy. This paper contributes to the growing body of knowledge by providing insights into how emerging technologies can be effectively applied in developing countries to overcome supply chain inefficiencies. The findings also highlight the importance of tailored technological adoption strategies that consider local socio-economic and infrastructural conditions.
Gamification in E-Learning: Transforming Education through Technology Domingo, Marianne Faith; Bautista, Carlo Andrew; Fajardo, Alyssa Jean
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.139

Abstract

The integration of gamification into e-learning has become a transformative force in education, particularly in developing countries such as the Philippines. This study explores the impact of gamified e-learning platforms on student engagement and academic performance in higher education institutions in the Philippines. Using a mixed-methods approach, data were collected from 150 undergraduate students across three universities through surveys, interviews, and academic performance records. The results show that 82% of participants reported increased motivation and engagement when using gamified learning platforms, and 67% demonstrated improved academic performance compared to those using traditional e-learning methods. Students particularly responded positively to elements such as badges, leaderboards, and point-based systems, which enhanced their sense of competition and achievement. Despite some challenges in implementation—such as internet accessibility and the need for culturally relevant game design—the study concludes that gamification holds significant potential to improve the effectiveness of e-learning in the Philippine educational context.

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